Techniques to Predict Interactions Utilizing Hidden Markov Models

Recent years have seen developments in hardware and software platforms managing access to computing applications. For example, many entities provide user account management software to manage user accounts associated with client applications via various subscriptions. When managing access and interactions by many user accounts in connection with many different computing applications, each of which can have different sets of possible permissions, determining when a given user account is likely to interact with a particular computing application can be an important and challenging aspect of managing hardware and software resources and availability. There is a need for accurately predicting account interactions with computing applications to ensure that sufficient server and network resources (e.g., computer memory, bandwidth) are available to handle processing loads associated with the use of the computing applications.

Embodiments are generally directed to artificial intelligence (AI) techniques for predicting account interactions between client computers and computing applications over a defined time period. Some embodiments are particularly directed to an account interaction prediction system implementing one or more machine learning (ML) models arranged to generate one or more predicted account interaction metrics. A non-limiting example of a suitable ML model comprises a hidden Markov model. The predicted account interaction metrics represent predicted account interactions between client computers and computing applications executing on one or more servers over a defined time period. In one embodiment, for example, a predicted account interaction metric represents a probability of a user to authorize activation, inactivation, or retention of one or more subscriptions, via a client computer, to one or more products or services provided by one or more computing applications based on the predicted account interaction metric.

In some embodiments, an account management system uses the predicted account interaction metric to calculate one or more key performance indicators (KPIs) for an entity that makes, uses, sells, or owns the product or services. In one embodiment, for example, a KPI comprises a financial KPI, such as a present value of future cash flows expected from the subscriptions over a defined time period. The AI techniques overcome certain limitations of conventional techniques by providing a more flexible and accurate measurement relative to other financial KPIs, such as annual recurring revenue (ARR), for example. The entity uses the improved financial KPI for operations such as monitoring budget spend, managing marketing campaigns, or measuring retention quality of customer cohorts.

Some embodiments utilize, among other ML models, one or more customized hidden Markov models. For example, the account interaction prediction system utilizes individualized user account data to generate individual-level hidden Markov models for predicting the user account interactions with computing applications. For instance, in some implementations, the disclosed systems utilize user account data of a user account to generate a set of hidden Markov model matrices, such as an initial hidden state probability matrix, a transition probability matrix, and an emission probability matrix for a customized hidden Markov model for the user account.

To illustrate, in some embodiments, the disclosed systems generate the matrices for the customized hidden Markov model utilizing one or more neural networks. For example, in some cases, the disclosed systems generate the initial hidden state probability matrix utilizing an initial state neural network, the transition probability matrix utilizing a transition neural network, and the emission probability matrix utilizing an emission neural network. Furthermore, in some embodiments, the disclosed systems utilize the generated matrices of the customized hidden Markov model to determine one or more predicted account interaction metrics for the user account indicating predicted future interactions of the user account with one or more computing applications for one or more time periods.

In some embodiments, the account interaction prediction module generates the hidden Markov model matrices, which comprise probability values corresponding to hidden states (which are unobservable) and/or outcome states (which are observable) of the hidden Markov model. For instance, the account interaction prediction module generates a transition matrix comprising transition probability values indicating probabilities of moving from one given hidden state to another given hidden state. As another example, the account interaction prediction module generates an emission matrix comprising emission probability values indicating probabilities of moving from one given hidden state to one given outcome state. By customizing the matrices of a hidden Markov model according to user account data of a specific user account, the account prediction system determines hidden states, transitions, and outcome states based on the specific characteristics of the user account.

In some embodiments, based on the predicted outcome states of the hidden Markov model, the account interaction prediction module determines one or more predicted account interaction metrics. For example, the account interaction prediction module predicts user account events, such as activation of a subscription (e.g., new activation or re-activation), deactivation of a subscription (e.g., terminating or churning), and/or retention of a subscription (e.g., maintaining) to a computing application. In some implementations, the account interaction prediction module determines predicted account interaction metrics in connection with multiple computing applications (e.g., multiple access events). In some embodiments, the account interaction prediction module determines predicted account interaction metrics corresponding to different time periods or time scales, such as account purchases or subscriptions beginning at different times and/or ending at different times.

In some embodiments, the account interaction prediction module determines predicted account interaction metrics corresponding to account purchases or subscriptions beginning at different times and/or ending at different times or time scales. One example of a predicted account interaction metric is referred to as a lifetime value (LTV) prediction or a customer lifetime value (CLTV) (collectively referred to as an LTV). An LTV is a metric that describes an expected monetary value a customer would bring to an entity (e.g., a business) in a given time window (e.g., over 1 year, 3 years, 5 years, etc.). The LTV is a customer-level metric as it is obtained by aggregating subscription-level results (e.g., individual subscriptions). A customer-level metric provides a higher-level of insight and accuracy relative to simply computing a monetary value of each individual paid subscription. For example, the LTV includes metrics representing activities of a subscriber, such as the subscriber initiating a new subscription, canceling a subscription, switching from a first subscription to a second subscription, converting from a trial subscription to a paid subscription, maintaining multiple subscriptions simultaneously, restarting a previous subscription, and other relevant metrics. The subscription-level information for a given customer is then aggregated to customer-level information. The LTV is an important metric for customer relationship management (CRM). It can be used to assist in monitoring budget spend, managing marketing campaigns, or measuring retention quality of customer cohorts. Paid LTV prediction refers to predicting the LTV of user accounts with active subscriptions (e.g., paid users).

Any of the above embodiments may be implemented as instructions stored on a non-transitory computer-readable storage medium and/or embodied as an apparatus with a memory and a processor configured to perform the actions described above. It is contemplated that these embodiments may be deployed individually to achieve improvements in resource requirements and library construction time. Alternatively, any of the embodiments may be used in combination with each other in order to achieve synergistic effects, some of which are noted above and elsewhere herein.

Embodiments are generally directed to artificial intelligence (AI) techniques for predicting account interactions between client computers and computing applications over a defined time period. Some embodiments are particularly directed to an account interaction prediction system implementing one or more machine learning (ML) models arranged to generate one or more predicted account interaction metrics. A non-limiting example of a suitable ML model comprises a hidden Markov model. The predicted account interaction metrics represent predicted account interactions between client computers and computing applications executing on one or more servers over a defined time period. In one embodiment, for example, a predicted account interaction metric represents a probability of a user to authorize activation, inactivation, or retention of one or more subscriptions, via a client computer, to one or more products or services provided by one or more computing applications based on the predicted account interaction metric. An account management system uses the predicted account interaction metric to calculate one or more key performance indicators (KPIs) for an entity that makes, uses, sells, or owns the product or services. In one embodiment, for example, a KPI comprises a financial KPI, such as a present value of future cash flows expected from the subscriptions over a defined time period. The AI techniques overcome certain limitations of conventional techniques by providing a more flexible and accurate measurement relative to other financial KPIs, such as annual recurring revenue (ARR), for example. The entity uses the improved financial KPI for operations such as monitoring budget spend, managing marketing campaigns, or measuring retention quality of customer cohorts. Although exemplary embodiments are described in connection with a particular AI system, the principles described herein can also be applied to other types of AI systems as well. Embodiments are not limited in this context.

Conventional prediction systems attempt to predict account interactions with computing applications. Such conventional systems suffer from a number of technical deficiencies, including inflexibility by imposing rigid assumptions on the models that constrain the models to a small set of real-world applications, inaccuracy by generating imprecise predictions, and inefficiency by utilizing excessive data storage, memory, and computing resources. For instance, conventional systems inflexibly constrain their prediction models with rigid and unrealistic assumptions. For example, conventional systems often utilize sub-models to predict factors that influence user account interactions, thereby limiting prediction model robustness because of variability of account interactions. Additionally, conventional systems often cannot generate accurate predictions for time periods different from training dataset periods, thereby limiting prediction robustness for different time scales without training separate models for the different time scales. Further, conventional systems inaccurately generate predictions of account interactions. In particular, conventional systems often lack in-depth insight into the dynamics of account interactions with computing applications, thereby missing interactions and yielding incomplete predictions. For instance, conventional systems often generate predictions that do not account for variations such as interactions by user accounts with subsets of computing applications. Inaccurately generating predictions of account interactions with computing applications results in inaccurate determinations of hardware, software, and network resource requirements for handling user account interactions for the computing applications. In addition, conventional systems often require large datasets of historical data to generate meaningful predictions. For example, conventional systems often require years of historical account data to generate predictions of account interactions for future years, which can result in unreliable and outdated predictions given frequently inconsistent records maintained by entities. Processing such large amounts of data, conventional systems often expend excessive computing time and resources (e.g., memory, storage space, and processing bandwidth). By processing large amounts of historical data, conventional systems often require extensive user interactions to preprocess, clean, and maintain historical data from multiple legacy systems, which can also lead to data inaccuracies, outdated predictions, and unreliable results.

Embodiments of the present disclosure provide solve these and other challenges associated with managing user account interactions with computing applications by, at least in part, utilizing customized hidden Markov models. In some embodiments, the disclosed systems utilize individualized user account data to generate individual-level hidden Markov models for predicting the user account interactions with computing applications. For instance, in some implementations, the disclosed systems utilize user account data of a user account to generate a set of hidden Markov model matrices, such as an initial hidden state probability matrix, a transition probability matrix, and an emission probability matrix for a customized hidden Markov model for the user account. To illustrate, in some embodiments, the disclosed systems generate the matrices for the customized hidden Markov model utilizing one or more neural networks. For example, in some cases, the disclosed systems generate the initial hidden state probability matrix utilizing an initial state neural network, the transition probability matrix utilizing a transition neural network, and the emission probability matrix utilizing an emission neural network. Furthermore, in some embodiments, the disclosed systems utilize the generated matrices of the customized hidden Markov model to determine one or more predicted account interaction metrics for the user account indicating predicted future interactions of the user account with one or more computing applications for one or more time periods.

The account interaction prediction module generates the hidden Markov model matrices, which comprise probability values corresponding to hidden states (which are unobservable) and/or outcome states (which are observable) of the hidden Markov model. For instance, the account interaction prediction module generates a transition matrix comprising transition probability values indicating probabilities of moving from one given hidden state to another given hidden state. As another example, the account interaction prediction module generates an emission matrix comprising emission probability values indicating probabilities of moving from one given hidden state to one given outcome state. By customizing the matrices of a hidden Markov model according to user account data of a specific user account, the account prediction system determines hidden states, transitions, and outcome states based on the specific characteristics of the user account.

Based on the predicted outcome states of the hidden Markov model, the account interaction prediction module determines one or more predicted account interaction metrics. For example, the account interaction prediction module predicts user account events, such as activation of a subscription (e.g., new activation or re-activation), deactivation of a subscription (e.g., terminating or churning), and/or retention of a subscription (e.g., maintaining) to a computing application. In some implementations, the account interaction prediction module determines predicted account interaction metrics in connection with multiple computing applications (e.g., multiple access events). In some embodiments, the account interaction prediction module determines predicted account interaction metrics corresponding to different time periods or time scales, such as account purchases or subscriptions beginning at different times and/or ending at different times.

In some embodiments, the account interaction prediction module determines predicted account interaction metrics corresponding to account purchases or subscriptions beginning at different times and/or ending at different times or time scales. One example of a predicted account interaction metric is referred to as a lifetime value (LTV) prediction or a customer lifetime value (CLTV) (collectively referred to as an LTV). An LTV is a metric that describes an expected monetary value a customer would bring to an entity (e.g., a business) in a given time window (e.g., over 1 year, 3 years, 5 years, etc.). The LTV is a customer-level metric as it is obtained by aggregating subscription-level results (e.g., individual subscriptions). A customer-level metric provides a higher-level of insight and accuracy relative to simply computing a monetary value of each individual paid subscription. For example, the LTV includes metrics representing activities of a subscriber, such as the subscriber initiating a new subscription, canceling a subscription, switching from a first subscription to a second subscription, converting from a trial subscription to a paid subscription, maintaining multiple subscriptions simultaneously, restarting a previous subscription, and other relevant metrics. The subscription-level information for a given customer is then aggregated to customer-level information. The LTV is an important metric for customer relationship management (CRM). It can be used to assist in monitoring budget spend, managing marketing campaigns, or measuring retention quality of customer cohorts. Paid LTV prediction refers to predicting the LTV of user accounts with active subscriptions (e.g., paid users).

The account interaction prediction module provides a variety of benefits relative to conventional systems. For example, the account interaction prediction module can predict LTV based on user account data using the hidden Markov model for current and future subscriptions, considering such factors as a user account having multiple subscriptions, relationships between subscriptions, switching between subscriptions, conversion to new subscriptions, and other metrics. This is an advantage over conventional techniques that merely focus on subscription-level predictions. Subscription-level predictions assume that all subscriptions are independent. But one subscriber can have multiple paid subscriptions at the same time, indicating that the independence assumption might not necessarily hold. A subscription-level predictor is not able to capture the connections or relationships among different subscriptions. Further, a subscription-level predictor relies on a price map that is calculated by averaging revenues over historical data and is fixed for all users. However, different subscribers could have different price maps due to different user attributes. For instance, pricing of a same product varies across different regions or certain customers receive discounts which changes the pricing. In addition, conventional techniques use shorter-term metrics (e.g., 1 month) or longer-term metrics (e.g., 1 year) based on subscriber behavior to estimate longer-term metrics (e.g., 1-3 years) without factoring in changes to the subscriber behavior, leading to inaccurate estimates. In another example, embodiments implement a hidden Markov model architecture that is designed to better represent subscriber behavior based on features such as historical engagement data, attributes, subscription information, and short-term observed subscription status. As a result, the HMM model does not require long-term true LTV as labels in training but can infer long-term subscription probabilities once the dynamic can be described by the hidden Markov model matrices. Further, the HMM model implements techniques such as a price map representing a true short-term LTV to learn a model to predict prices for each subscriber from input features associated with a subscriber, an ensemble of multiple binary-classification HMM sub-models that share a same initial hidden states matrix and transition matrix, so that the dependence of different subscriptions can be captured, and/or a transformer encoder to perform flexible retention curve modeling to solve for changes in retention probability that may not necessarily fit an exponential decay curve.

As illustrated by the foregoing discussion, the present disclosure utilizes a variety of terms to describe features and advantages of the account interaction prediction module. Additional detail is now provided regarding the meaning of such terms. For example, as used herein, the term “user account” refers to an account or profile associated with a user of one or more computing applications. In particular, the term “user account” includes a user profile with information relating to a subscriber of the one or more computing applications. While this disclosure discusses various examples of a user account in terms of an account management system that manages use of computing applications, the systems disclosed herein are not limited to this example and can apply to any computer system that manages user accounts for any purposes.

As used herein, the term “user account data” refers to data or information about a user account. In particular, the term “user account data” includes identification data, user account profile data (e.g., demographic data or other attributes of a user of the user account), user account activity data, and user device activity data. To illustrate, user account data includes identifying information about a user of a computing application. For instance, user account data includes a name, email address, social media handle, or other unique identifying number or identifying information. Moreover, user account data includes account activity data, such as login activity associated with one or more computing applications, visits to the one or more computing applications, access history of a user account, support ticket metadata associated with a user account, and access data associated with the one or more computing applications. For example, user account data includes account activity such as access times and durations, access permissions, metadata of content accessed, opened, created, viewed, copied, modified, saved, downloaded, sent, and/or closed by the user account. User account data also includes device activity data, such as information identifying a client device associated with the user account. For instance, user account data includes user device activity data associated with the one or more computing applications, such as device access times and durations, and metadata of content accessed, opened, created, viewed, copied, modified, saved, downloaded, sent, and/or closed by the client device. Furthermore, user account data includes historical account interactions with the one or more computing applications.

As used herein, the term “computing application” refers to an application or software tool provided to a computing device. In particular, the term “computing application” includes a desktop application, a mobile application, and/or a web-based application. To illustrate, a computing application includes an application for document creation and editing, creative content creation and editing, and/or image and video editing.

As used herein, the terms “account interaction” or “account action” refer to an action performed by a user account or by a client device associated with the user account. In particular, the terms “account interaction” or “account action” include events and/or actions associated with one or more computing applications. To illustrate, an account interaction or account action includes logins for the one or more computing applications, access events by the user account or the client device to the one or more computing applications, and/or subscription statuses of the user account to the one or more computing applications.

As used herein, the term “hidden Markov model” (or “HMM”) refers to a model representing a Markov process. In particular, the term “hidden Markov model” includes customized (e.g., individual) hidden Markov models for user accounts. To illustrate, a hidden Markov model includes a model containing hidden states and outcome states. As used herein, the term “hidden state” refers to an unobservable state of the model, and the term “outcome state” refers to an observable state of the model. In particular, a hidden state is influenced by a previous hidden state, and an outcome state is influenced by a hidden state. For example, a hidden state indicates hidden dynamics of user account interactions with computing applications, while an outcome state indicates observable user account interactions.

As used herein, the term “initial state matrix” (or “initial hidden state probability matrix”) refers to a matrix of initial state values for a hidden Markov model. The term “initial state value” (or “initial state probability value”) refers to a metric of a probability that a user account exists at or within one hidden state of the HMM at a given time.

As used herein, the term “transition matrix” (or “transition probability matrix”) refers to a matrix of transition values for a hidden Markov model. The term “transition value” (or “transition probability value”) refers to a metric of a probability that a user account will transition from a first hidden state of the HMM to a second hidden state of the HMM (or, alternatively, remain at the one or more first hidden states), at a given time.

As used herein, the term “emission matrix” (or “emission probability matrix”) refers to a matrix of emission values for a hidden Markov model. The term “emission value” (or “emission probability value”) refers to a metric of a probability that a user account will emit one or more outcome states of the HMM based on having one hidden state of the HMM, at a given time.

As used herein, the term “predicted account interaction metric” refers to a metric indicating a probability of a particular account interaction occurring for a user account. In particular, the term “predicted account interaction metric” includes predictions that the user account will interact with one or more computing applications or that a specific activity occurs in connection with the user account and the one or more computing applications. To illustrate, a predicted account interaction metric includes, but is not limited to, a login, an access event, a change in access permissions, and/or a subscription to the one or more computing applications.

As used herein, the term “machine-learning model” refers to a computer representation that is tunable (e.g., trained) based on inputs to approximate unknown functions used for generating corresponding outputs. In particular, a machine-learning model includes a computer-implemented model that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs that reflect patterns and attributes of the known data. For instance, a machine learning model includes, but is not limited to, a neural network, a decision tree (e.g., a gradient boosted decision tree), association rule learning, inductive logic programming, support vector learning, Bayesian network, regression-based model (e.g., censored regression), principal component analysis, or a combination thereof.

As used herein, the term “neural network” refers to a class of tunable (e.g., trainable) machine-learning models that comprise interconnected artificial neurons (e.g., organized in layers) that communicate and learn to approximate complex functions and generate outputs. In particular, the term “neural network” includes an algorithm (or set of algorithms) that implements deep learning techniques (e.g., a deep neural network) to model high-level abstractions in data. For example, a neural network includes a convolutional neural network, a recurrent neural network (e.g., an LSTM), a graph neural network, or a generative adversarial neural network. To illustrate, a neural network includes a deep neural network that processes user account data to generate values and/or parameters of a hidden Markov model. In particular, a neural network extracts embeddings from the user account data to generate values of the HMM matrices.

As used herein, the term “feature” refers to a metric, value, characteristic, or property of a user account. For instance, in some cases, a feature includes a user account characteristic input into a neural network. As used herein, the term “embedding” refers to a metric, value, or vector generated by a neural network, and representing a feature of a user account. For example, in some cases, an embedding includes an output of a neural network based on user account data. To illustrate, the neural network is trained with historical user account data (e.g., in the form of user account features) to extract embeddings that represent the user account in the form of a model, such as the hidden Markov model.

As used herein, the term “time period” refers to temporal information about an account interaction, such as a predicted account interaction. In particular, the term “time period” includes a period of time and/or a time scale. To illustrate, a time period includes a start time, an end time, and/or a duration of an account interaction. For example, a time period includes a length of time of a subscription of a user account to one or more computing applications. As another example, a time period includes a starting time and ending time of the subscription of the user account to the one or more computing applications.

Additional detail will now be provided in relation to illustrative figures portraying example embodiments and implementations of an account interaction prediction module.

illustrates an account interaction prediction system. The account interaction prediction systemcomprises an example of an AI system (or environment) suitable for implementation by a computing device, such as a physical or virtual server device of a cloud-computing system, for example.

As depicted in, the account interaction prediction systemcomprises, inter alia, an account interaction prediction module. In some instances, the account interaction prediction modulereceives a request to determine a predicted account interaction metric. The request is received, for example, from a client device or an account management system. For example, the request includes an identification of a user accountand a query a current and predicted future interactions with one or more computing applications.

To illustrate, the account interaction prediction modulegenerates one or more hidden Markov model (HMM) matrices, such as HMM matricesof a hidden Markov modelbased on user account datafor the user account, and determines the predicted account interaction metricbased on the one or more HMM matrices. Some embodiments of a server device (not shown) are operated by a user to perform a variety of functions via the account management systemon the server device. For example, the server device, through the account interaction prediction module, on behalf of an account management system, performs functions such as, but not limited to, determining the user account dataassociated with the one or more computing applicationsfor the user account, utilizing the user account datato generate one or more HMM matricesof a customized hidden Markov model for the user account, and determining predicted account interaction metricsfor the user accountvia the customized hidden Markov model.

In one embodiment, for example, the account interaction prediction modulereceives a set of input features. A feature generation modulemay generate the input featuresto include features from a user account, user account dataassociated with the user account, and/or a set of computing applicationsassociated with the user account. For example, the input featuresinclude historical engagement data such as desktop application usage, mobile application usage, web visits, and so forth. In another example, the input featuresinclude user attributes such as country, signup source, age of subscription, and other demographic information. In yet another example, the input featuresincludes product attributes such as product names, promotion status for a subscription, entitlement period, and so forth. Additional examples of features are described with reference to. Embodiments are not limited to these examples.

As discussed above, in some embodiments, the account interaction prediction moduledetermines one or more predicted account interaction metrics. Specifically,shows the account interaction prediction moduleidentifying a user account, determining user account datafor the user account, generating a customized hidden Markov modelbased on the user account data, and determining the predicted account interaction metricutilizing the customized hidden Markov model.

As mentioned, in some implementations, the account interaction prediction moduleidentifies the user account. For instance, the account interaction prediction modulereceives an indication of the user account(e.g., from a client device), along with a request for the predicted account interaction metricfor the user account, such as from the account management system.

In some embodiments, the account interaction prediction moduledetermines the user account datafor the user account. For example, the account interaction prediction moduleretrieves account profile data, account activity data, and/or device activity data for the user accountthat indicates, among other things, past interactions with one or more computing applications. As explained in additional detail below, the account interaction prediction moduleutilizes the user account datato generate parameters of the customized hidden Markov modelthat are specific to the user account.

In some implementations, the account interaction prediction modulegenerates the customized hidden Markov modelbased on the user account data. For example, the account interaction prediction modulegenerates probability values for the customized hidden Markov modelassociated with the user account. Examples of probability values include, without limitation, initial state values, transition values, and emission values, as described below.

In some implementations, the account interaction prediction moduledetermines the predicted account interaction metricutilizing the customized hidden Markov model. For instance, the account interaction prediction modulesamples outcome states of the customized hidden Markov modelto generate the predicted account interaction metric. The account management systemuses the predicted account interaction metricto perform certain downstream actions. In one embodiment, for example, the account management systemsends a control directive for a computing resource allocationto allocate a set of computing resources for the set of computing applicationsbased on the predicted account interaction metric.

In one or more embodiments, the account interaction prediction moduleutilizes the customized hidden Markov modelto generate one or more predicted metrics indicating an estimated lifecycle of interactions of the user accountfor one or more computing applications. For example, the account interaction prediction modulegenerates the predicted account interaction metricto indicate an estimated state of the user accountwith respect to the one or more computing applications. Examples of estimated state include, without limitation, a transition state, conversion state, termination state, activation state, deactivation state, churn state or other states associated with one or more subscriptions, account access, purchase of a product or service, or other account activity.

In some embodiments, the account interaction prediction moduleutilizes the predicted account interaction metricto determine an LTV value of the user accountin terms of transition, conversion and/or churn of one or more subscriptions, purchases, or account accesses to one or more computing applications. To illustrate, the account interaction prediction moduleutilizes the estimated state of the user accountto determine whether the user accounthas a first set of access permissions (e.g., a set of access permissions associated with an unpaid account), a second set of access permissions (e.g., a set of access permissions associated with a paid account), or other set of access permissions (e.g., one of a plurality of tiers of access permissions) for a computing application. In some embodiments, the account interaction prediction modulealso determines the estimated lifecycle or LTV of interactions in connection with generating electronic messages to provide to a client device of the user accountfor one or more computing applications.

In one embodiment, for example, the account interaction prediction modulegenerates a predicted account interaction metricto determine an improved LTV value of a user accountin terms of transition, conversion and/or churn of one or more subscriptions, purchases, or account accesses to one or more computing applications. Calculating an LTV provides a more flexible measurement relative to conventional financial KPIs, such as annual recurring revenue (ARR), for example, as expressed in Equation (1) as follows:

This is due, in part, to Equation (1) computing a subscription-level monetary value of each individual paid subscription, which under-estimates a total monetary value. By way of contrast, the account interaction prediction modulegenerates a predicted account interaction metricthat represents a customer-level monetary value that aggregates the individual paid subscriptions to determine a more precise total monetary value for a given customer. This approach directly computes a total monetary value of each paying customer by, at least in part, collecting subscriptions of a customer to acquire customer-level data, interpolating data for any gaps in the customer-level data, and providing better prediction results over a defined time period (e.g., predicting a 3-year LTV).

LTV represents a present value of future cash flows expected from the subscriber during their relationship with the company. In this way, it is a similar KPI as ARR. ARR determines a value that a customer will bring to a company in a defined time period (e.g., 12 months) if the company retains the customer for the defined time period. Similarly, LTV also a company to determine a value that a customer will bring to the company in a defined time period. However, it does not assume that the company will retain the customer for the defined time period (e.g., the next 12 months). Rather, it considers a retention rate. In that sense, LTV is a more flexible version of ARR. By way of example, an LTV prediction describes the expected monetary value a customer would bring to the business in a given window (like 1 year, 3 years . . . etc.). In one embodiment, for example, the account interaction prediction moduleis arranged to calculate an LTV over a period of 36 months or 3 years. Further, the account interaction prediction moduleis capable of predicting a 3-year LTV while using training data from a shorter time period, such as 1-year LTV training data. This solves a problem of attempting to calculate a 3-year LTV using the most recent 3-year LTV training data, which would necessarily include at least a 1-year LTV gap in the training data, thereby causing use of stale data.

Conventional techniques focus on estimating the lifespans of customers' existing subscriptions, which is subscription-level churn probability predictions. However, it has several limitations. For example, some subscriptions of paid users cannot be covered by conventional techniques. As a subscription-level predictor, conventional techniques focus on paid subscriptions. Paid users are the ones who have paid subscriptions on the scoring date. However, within the prediction window, a paid user could switch some of her paid subscriptions to other subscriptions or convert to other new subscriptions. In the cases of switch and new conversion, both new subscriptions are not even paid subscriptions on the scoring date, so they are not accessible to the current method, thus are not covered. In another example, subscription-level predictions assume that all subscriptions are independent. However, a single user can have multiple paid subscriptions at the same time, indicating that the independence assumption might not necessarily hold. As a subscription-level predictor, conventional techniques are not able to capture the connections or relationships among different subscriptions. In yet another example, after the churn probabilities (lifespans) are predicted, a price map holding revenues of different subscriptions is needed to convert them into LTV values. However, a price map is typically calculated by averaging revenues over historical data and is fixed for all users. But different users could have different price maps due to different user attributes. For instance, the pricing of the same product varies across different regions, or customers may get discounts which also changes the pricing. In still another example, conventional techniques make strong assumptions on customers' propensities. Since customers' lifespan estimation (e.g., churn probability prediction) does not need true LTV as labels, the model can be trained using data of short amount of time (e.g., one month for instance). But to use this one-month churn probability predictor to predict long-term lifespan, a large amount of users' propensities is assumed to stay the same during the prediction window.

The account interaction prediction moduleaddresses these and other challenges by predicting both short-term and long-term paid LTV on a customer level. The account interaction prediction moduleuses a hidden Markov modelthat utilizes a deep neural network framework to describe the dynamic nature of paid users' subscription behavior. The hidden Markov modeluses three HMM matricesto describe the dynamic of a user's subscription behavior and the motivation behind such behavior. The HMM matricesinclude an initial hidden state matrix, a transition matrix, and an emission matrix. A motivation behind users' subscription behavior is modeled by the hidden Markov modelhidden states and transition matrix. The subscription behavior is modeled by the hidden Markov modeloutcome states and emission matrix. These three matrices are learned through such features as a users' historical engagement data, attributes, subscription information, and short-term observed subscription status. As a result, the hidden Markov modeldoes not require long-term true LTV as labels in training but can infer long-term subscription probabilities once the dynamic can be described by the HMM matrices.

To predict subscription probabilities of potentially multiple subscriptions of a given user, and consider price variation among user LTV, the account interaction prediction moduleimplements several modules to the hidden Markov modelframework, including a price mapping module, a multi-label classification module, and a transformer encoder module.

The price mapping module customizes pricing information for a specific user. Since different users might hold different product prices, the hidden Markov modelof the account interaction prediction moduleutilizes a true short-term LTV to learn a model to predict prices for each user from the user's input features.

The multi-label classification module allows the hidden Markov modelto perform multi-label classification. On the customer level, users can have multiple subscriptions at the same time, revealing the multi-label classification nature of the task. This incurs a challenge because conventional techniques are used to solve multi-class classification problems. The multi-label classification module provides a solution for the multi-label problem to predict subscription probabilities of multiple subscriptions at the time. The hidden Markov modelframework is an ensemble of multiple binary-classification hidden Markov model sub-models. The multiple binary-classification hidden Markov model sub-models share the same initial hidden states matrix and transition matrix, so that the dependence of different subscriptions can be captured.